

import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase			:				List[str]   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_=None     ,				lowerCamelCase_=None		):
							return field(default_factory=lambda: default     ,				metadata=lowerCamelCase_		)







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		List[str]								=	list_field(
							    default=[]		,       metadata={
							        '''help''': (
							            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
							            ''' of all available models'''
							        )
							    }		,       )

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8]		,       metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}				)

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8, 32, 1_28, 5_12]		,       metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}		,       )

							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Use FP16 to accelerate inference.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Benchmark training of model'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Verbose memory tracing'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
							    }		,       )
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Trace memory line by line'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save result to a CSV file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save all print statements in a log file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to print environment information'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
							            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
							            ''' for debugging / testing and on TPU.'''
							        )
							    }		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'env_info_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving environment information.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'log_{round(time()				)}.csv'		,       metadata={'''help''': '''Log filename used if print statements are saved in log.'''}		,       )
							UpperCAmelCase_						:		int								=	field(default=3		,       metadata={'''help''': '''Times an experiment will be run.'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
							            ''' model weights.'''
							        )
							    }		,       )




							def       snake_case			(					self						)		->       Union[str, Any]:
														warnings.warn(
														    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
														    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
														    ''' to benchmark Transformer models.'''    ,						__UpperCAmelCase    ,						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														return json.dumps(dataclasses.asdict(self						)    ,						indent=2						)




							@property
							def       snake_case			(					self						)		->       List[str]:
														if len(self.models						) <= 0:
																					raise ValueError(
																					    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																					    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''						)
														return self.models






							@property
							def       snake_case			(					self						)		->       int:
														if not self.multi_process:
																					return False
														elif self.is_tpu:
																					logger.info('''Multiprocessing is currently not possible on TPU.'''						)
																					return False
														else:
																					return True









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase			:				Tuple   =     logging.get_logger(__name__)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''pixel_values''']




							def __init__(					self    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 1 / 2_55    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       None:
														super().__init__(**__UpperCAmelCase						)
														A      :					Any      		=					size if size is not None else {'''height''': 2_56, '''width''': 2_56}
														A      :					Any      		=					get_size_dict(__UpperCAmelCase						)
														A      :					List[Any]      		=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Dict      		=					do_resize
														A      :					Tuple      		=					size
														A      :					Union[str, Any]      		=					resample
														A      :					Dict      		=					do_center_crop
														A      :					int      		=					crop_size
														A      :					Union[str, Any]      		=					do_rescale
														A      :					str      		=					rescale_factor
														A      :					Optional[Any]      		=					do_normalize
														A      :					Dict      		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														A      :					Dict      		=					image_std if image_std is not None else IMAGENET_STANDARD_STD




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					int      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return resize(
														    __UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						resample=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					Optional[Any]      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return center_crop(__UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       List[str]:
														return rescale(__UpperCAmelCase    ,						scale=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														return normalize(__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = ChannelDimension.FIRST    ,						**__UpperCAmelCase    ,						)		->       PIL.Image.Image:
														A      :					Optional[int]      		=					do_resize if do_resize is not None else self.do_resize
														A      :					Optional[Any]      		=					resample if resample is not None else self.resample
														A      :					Tuple      		=					do_center_crop if do_center_crop is not None else self.do_center_crop
														A      :					Optional[Any]      		=					do_rescale if do_rescale is not None else self.do_rescale
														A      :					str      		=					rescale_factor if rescale_factor is not None else self.rescale_factor
														A      :					Tuple      		=					do_normalize if do_normalize is not None else self.do_normalize
														A      :					Optional[int]      		=					image_mean if image_mean is not None else self.image_mean
														A      :					Dict      		=					image_std if image_std is not None else self.image_std

														A      :					List[Any]      		=					size if size is not None else self.size
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase						)
														A      :					Dict      		=					crop_size if crop_size is not None else self.crop_size
														A      :					Tuple      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Any      		=					make_list_of_images(__UpperCAmelCase						)

														if not valid_images(__UpperCAmelCase						):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

														if do_resize and size is None or resample is None:
																					raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

														# All transformations expect numpy arrays.
														A      :					List[Any]      		=					[to_numpy_array(__UpperCAmelCase						) for image in images]

														if do_resize:
																					A      :					List[str]      		=					[self.resize(image=__UpperCAmelCase    ,						size=__UpperCAmelCase    ,						resample=__UpperCAmelCase						) for image in images]

														if do_center_crop:
																					A      :					Dict      		=					[self.center_crop(image=__UpperCAmelCase    ,						size=__UpperCAmelCase						) for image in images]

														if do_rescale:
																					A      :					Union[str, Any]      		=					[self.rescale(image=__UpperCAmelCase    ,						scale=__UpperCAmelCase						) for image in images]

														if do_normalize:
																					A      :					Tuple      		=					[self.normalize(image=__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase						) for image in images]

														A      :					Optional[Any]      		=					[to_channel_dimension_format(__UpperCAmelCase    ,						__UpperCAmelCase						) for image in images]

														A      :					List[Any]      		=					{'''pixel_values''': images}
														return BatchFeature(data=__UpperCAmelCase    ,						tensor_type=__UpperCAmelCase						)






